Netflix's global subscriber base has reached an impressive milestone, surpassing *** million paid subscribers worldwide in the fourth quarter of 2024. This marks a significant increase of nearly ** million subscribers compared to the previous quarter, solidifying Netflix's position as a dominant force in the streaming industry. Adapting to customer losses Netflix's growth has not always been consistent. During the first half of 2022, the streaming giant lost over *** million customers. In response to these losses, Netflix introduced an ad-supported tier in November of that same year. This strategic move has paid off, with the lower-cost plan attracting ** million monthly active users globally by November 2024, demonstrating Netflix's ability to adapt to changing market conditions and consumer preferences. Global expansion Netflix continues to focus on international markets, with a forecast suggesting that the Asia Pacific region is expected to see the most substantial growth in the upcoming years, potentially reaching around **** million subscribers by 2029. To correspond to the needs of the non-American target group, the company has heavily invested in international content in recent years, with Korean, Spanish, and Japanese being the most watched non-English content languages on the platform.

Analysis of ‘Netflix subscribers and revenue by country’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/pariaagharabi/netflix2020 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

I prepare this dataset for one of my courses to show how Netflix’s subscription figures and Netflix's revenue($) have grown in four different regions: - the United States and Canada, - Europe, the Middle East, and Africa, - Latin America, - Asia-Pacific over the last 2.5 years. According to the final month of the quarter 2020(March) was being the start of the global coronavirus pandemic in many countries, Netflix noted that it added 26 million paid new subscribers in the first two quarters of 2020 alone; in 2019, the company added 28 million subscribers in total.

Dataset Description: This dataset contains four CSV files. 1. DataNetflixRevenue2020_V2.csv: three columns Area, Years, Revenue.

1. DataNetflixSubscriber2020_V2.csv: three columns Area, Years, Subscribers.

2. NetflixSubscribersbyCountryfrom2018toQ2_2020.csv: eleven columns Area, Q1 - 2018, Q2 - 2018, Q3 - 2018, Q4 - 2018, Q1 - 2019, Q2 - 2019, Q3 - 2019, Q4 - 2019, Q1 - 2020, Q2 - 2020

3. Netflix'sRevenue2018toQ2_2020.csv: eleven columns Area, Q1 - 2018, Q2 - 2018, Q3 - 2018, Q4 - 2018, Q1 - 2019, Q2 - 2019, Q3 - 2019, Q4 - 2019, Q1 - 2020, Q2 - 2020

--- Original source retains full ownership of the source dataset ---

Context

Netflix, Inc. is an American media company engaged in paid streaming and the production of films and series.

Market cap

Market capitalization of Netflix (NFLX)

Market cap: $517.08 Billion USD

As of June 2025 Netflix has a market cap of $517.08 Billion USD. This makes Netflix the world's 19th most valuable company by market cap according to our data. The market capitalization, commonly called market cap, is the total market value of a publicly traded company's outstanding shares and is commonly used to measure how much a company is worth.

Revenue

Revenue for Netflix (NFLX)

Revenue in 2025: $40.17 Billion USD

According to Netflix's latest financial reports the company's current revenue (TTM ) is $40.17 Billion USD. In 2024 the company made a revenue of $39.00 Billion USD an increase over the revenue in the year 2023 that were of $33.72 Billion USD. The revenue is the total amount of income that a company generates by the sale of goods or services. Unlike with the earnings no expenses are subtracted.

Earnings

Earnings for Netflix (NFLX)

Earnings in 2025 (TTM): $11.31 Billion USD

According to Netflix's latest financial reports the company's current earnings are $40.17 Billion USD. In 2024 the company made an earning of $10.70 Billion USD, an increase over its 2023 earnings that were of $7.02 Billion USD. The earnings displayed on this page is the company's Pretax Income.

End of Day market cap according to different sources

On Jun 12th, 2025 the market cap of Netflix was reported to be:

$517.08 Billion USD by Yahoo Finance

$517.08 Billion USD by CompaniesMarketCap

$517.21 Billion USD by Nasdaq

Content

Geography: USA

Time period: May 2002- June 2025

Unit of analysis: Netflix Stock Data 2025

Variables

Acknowledgements

This dataset belongs to me. I’m sharing it here for free. You may do with it as you wish.

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

Analysis of ‘1000 Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/chasewillden/netflix-shows on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Content

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Acknowledgements

The data set and the research article can be found at The Concept Center

Inspiration

I was watching Netflix with my wife and we asked ourselves, why are there so many R and TV-MA rating shows?

--- Original source retains full ownership of the source dataset ---

Here is the full breakdown of Netflix global subscribers by year since 2013.

About this Dataset: Disney+ is another one of the most popular media and video streaming platforms. They have close to 1300 movies or tv shows available on their platform, as of mid-2021, they have over 116M Subscribers globally. This tabular dataset consists of listings of all the movies and tv shows available on Amazon Prime, along with details such as - cast, directors, ratings, release year, duration, etc.

Interesting Task Ideas

1. Understanding what content is available in different countries
2. Identifying similar content by matching text-based features
3. Network analysis of Actors / Directors and find interesting insights
4. Does Disney+ has more focus on TV Shows than movies in recent years.

Other Platform's Datasets (Click on the logos to view)

![alt text][1] ![alt text][3] ![alt text][5] ![alt text][7] [1]: https://i.imgur.com/As0PMcL.jpg =75x20

[3]: https://i.imgur.com/r5t3MpQ.jpg =75x20

[5]: https://i.imgur.com/4a4ZMuy.png =75x30

[7]: https://i.imgur.com/nCL8Skc.png?1 =75x32

• Amazon Prime Video Movies and TV Shows
• Disney+ Movies and TV Shows
• Netflix Prime Video Movies and TV Shows
• Hulu Movies and TV Shows

In this post, you'll see how the Netflix platform is evolving, how many users Netflix has and how they perform against the growing competition.

Industry data revealed that Slovakia had the most extensive Netflix media library worldwide as of July 2024, with over 8,500 titles available on the platform. Interestingly, the top 10 ranking was spearheaded by European countries. Where do you get the most bang for your Netflix buck? In February 2024, Liechtenstein and Switzerland were the countries with the most expensive Netflix subscription rates. Viewers had to pay around 21.19 U.S. dollars per month for a standard subscription. Subscribers in these countries could choose from between around 6,500 and 6,900 titles. On the other end of the spectrum, Pakistan, Egypt, and Nigeria are some of the countries with the cheapest Netflix subscription costs at around 2.90 to 4.65 U.S. dollars per month. Popular content on Netflix While viewing preferences can differ across countries and regions, some titles have proven particularly popular with international audiences. As of mid-2024, "Red Notice" and "Don't Look Up" were the most popular English-language movies on Netflix, with over 230 million views in its first 91 days available on the platform. Meanwhile, "Troll" ranks first among the top non-English language Netflix movies of all time. The monster film has amassed 103 million views on Netflix, making it the most successful Norwegian-language film on the platform to date.

Analysis of ‘Netflix TV Series Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/harshitshankhdhar/netflix-and-amazon-prime-tv-series-dataset on 13 February 2022.

--- Dataset description provided by original source is as follows ---

Context

This data is scraped from wikipedia site.

Content

• Title - Name of the Series.
• Genre - Genre of the series.
• Premiere - Released Year
• No_of_Seasons - Total number of Seasons
• No_of_Episodes - Total number of episodes.

Inspiration

• How many seasons and episodes a series have?
• When it released?
• What are the different genre of the TV Series?

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Netflix Shows’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/netflix-showse on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Background

Netflix in the past 5-10 years has captured a large populate of viewers. With more viewers, there most likely an increase of show variety. However, do people understand the distribution of ratings on Netflix shows?

Netflix Suggestion Engine

Because of the vast amount of time it would take to gather 1,000 shows one by one, the gathering method took advantage of the Netflix’s suggestion engine. The suggestion engine recommends shows similar to the selected show. As part of this data set, I took 4 videos from 4 ratings (totaling 16 unique shows), then pulled 53 suggested shows per video. The ratings include: G, PG, TV-14, TV-MA. I chose not to pull from every rating (e.g. TV-G, TV-Y, etc.).

Source

Access to the study can be found at The Concept Center

This dataset was created by Chase Willden and contains around 1000 samples along with User Rating Score, Rating Description, technical information and other features such as: - Release Year - Title - and more.

How to use this dataset

• Analyze User Rating Size in relation to Rating
• Study the influence of Rating Level on User Rating Score
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Chase Willden

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Overview

Streaming Services Statistics: Streaming services have transformed the entertainment landscape, revolutionizing how people consume content.

The advent of high-speed internet and the proliferation of smart devices have fueled the growth of these platforms, offering a wide array of movies, TV shows, music, and more, at the viewers' convenience.

This introduction provides an overview of key statistics that shed light on the impact, trends, and challenges within the streaming industry.

Analysis of ‘Netflix "Top 10" TV Shows and Films’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/dhruvildave/netflix-top-10-tv-shows-and-films on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Every Tuesday, Netflix publishes four global Top 10 lists for films and TV: Film (English), TV (English), Film (Non-English), and TV (Non-English). These lists rank titles based on weekly hours viewed: the total number of hours that members around the world watched each title from Monday to Sunday of the previous week.

Each season of a series and each film is considered on their own, so you might see both Stranger Things seasons 2 and 3 in the Top 10. Because titles sometimes move in and out of the Top 10, there is also the total number of weeks that a season of a series or film has spent on the list.

Netflix also publishes Top 10 lists for nearly 100 countries and territories (the same locations where there are Top 10 rows on Netflix). Country lists are also ranked based on hours viewed but don’t show country-level viewing directly.

Finally, Netflix provides a list of the Top 10 most popular Netflix films and TV (branded Netflix in any country) in each of the four categories based on the hours that each title was viewed during its first 28 days.

--- Original source retains full ownership of the source dataset ---

This dataset consists of tv shows and movies available on Netflix as of 2019. The dataset is collected from Flixable which is a third-party Netflix search engine.

In 2018, they released an interesting report which shows that the number of TV shows on Netflix has nearly tripled since 2010. The streaming service’s number of movies has decreased by more than 2,000 titles since 2010, while its number of TV shows has nearly tripled. It will be interesting to explore what all other insights can be obtained from the same dataset.

Integrating this dataset with other external datasets such as IMDB ratings, rotten tomatoes can also provide many interesting findings.

Inspiration Some of the interesting questions (tasks) which can be performed on this dataset -

Understanding what content is available in different countries Identifying similar content by matching text-based features Network analysis of Actors / Directors and find interesting insights Is Netflix has increasingly focusing on TV rather than movies in recent years?

Context

Netflix held the Netflix Prize open competition for the best algorithm to predict user ratings for films. The grand prize was $1,000,000 and was won by BellKor's Pragmatic Chaos team. This is the dataset that was used in that competition.

Content

This comes directly from the README:

TRAINING DATASET FILE DESCRIPTION

The file "training_set.tar" is a tar of a directory containing 17770 files, one per movie. The first line of each file contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format:

CustomerID,Rating,Date

• MovieIDs range from 1 to 17770 sequentially.
• CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users.
• Ratings are on a five star (integral) scale from 1 to 5.
• Dates have the format YYYY-MM-DD.

MOVIES FILE DESCRIPTION

Movie information in "movie_titles.txt" is in the following format:

MovieID,YearOfRelease,Title

• MovieID do not correspond to actual Netflix movie ids or IMDB movie ids.
• YearOfRelease can range from 1890 to 2005 and may correspond to the release of corresponding DVD, not necessarily its theaterical release.
• Title is the Netflix movie title and may not correspond to titles used on other sites. Titles are in English.

QUALIFYING AND PREDICTION DATASET FILE DESCRIPTION

The qualifying dataset for the Netflix Prize is contained in the text file "qualifying.txt". It consists of lines indicating a movie id, followed by a colon, and then customer ids and rating dates, one per line for that movie id. The movie and customer ids are contained in the training set. Of course the ratings are withheld. There are no empty lines in the file.

MovieID1:

CustomerID11,Date11

CustomerID12,Date12

...

MovieID2:

CustomerID21,Date21

CustomerID22,Date22

For the Netflix Prize, your program must predict the all ratings the customers gave the movies in the qualifying dataset based on the information in the training dataset.

The format of your submitted prediction file follows the movie and customer id, date order of the qualifying dataset. However, your predicted rating takes the place of the corresponding customer id (and date), one per line.

For example, if the qualifying dataset looked like:

111:

3245,2005-12-19

5666,2005-12-23

6789,2005-03-14

225:

1234,2005-05-26

3456,2005-11-07

then a prediction file should look something like:

111:

3.0

3.4

4.0

225:

1.0

2.0

which predicts that customer 3245 would have rated movie 111 3.0 stars on the 19th of Decemeber, 2005, that customer 5666 would have rated it slightly higher at 3.4 stars on the 23rd of Decemeber, 2005, etc.

You must make predictions for all customers for all movies in the qualifying dataset.

THE PROBE DATASET FILE DESCRIPTION

To allow you to test your system before you submit a prediction set based on the qualifying dataset, we have provided a probe dataset in the file "probe.txt". This text file contains lines indicating a movie id, followed by a colon, and then customer ids, one per line for that movie id.

MovieID1:

CustomerID11

CustomerID12

...

MovieID2:

CustomerID21

CustomerID22

Like the qualifying dataset, the movie and customer id pairs are contained in the training set. However, unlike the qualifying dataset, the ratings (and dates) for each pair are contained in the training dataset.

If you wish, you may calculate the RMSE of your predictions against those ratings and compare your RMSE against the Cinematch RMSE on the same data. See http://www.netflixprize.com/faq#probe for that value.

Acknowledgements

The training data came in 17,000+ files. In the interest of keeping files together and file sizes as low as possible, I combined them into four text files: combined_data_(1,2,3,4).txt

The contest was originally hosted at http://netflixprize.com/index.html

The dataset was downloaded from https://archive.org/download/nf_prize_dataset.tar

Inspiration

This is a fun dataset to work with. You can read about the winning algorithm by BellKor's Pragmatic Chaos here

This dataset offers a comprehensive historical record of Netflix’s stock price movements, capturing the company’s financial journey from its early days to its position as a global streaming giant.

From its IPO in May 2002, Netflix (Ticker: NFLX) has transformed from a DVD rental service to a powerhouse in on-demand digital content. With its disruptive innovation, strategic shifts, and global expansion, Netflix has seen dramatic shifts in stock prices, reflecting not just market trends but also cultural impact. This dataset provides a window into that evolution.

What’s Included?

Each row in this dataset represents daily trading activity on the stock market and includes the following columns:

• Date – The trading day (from 2002 onward)
• Open – Stock price when the market opened
• High – Highest trading price of the day
• Low – Lowest trading price of the day
• Close – Final price at market close
• Adj Close – Closing price adjusted for splits and dividends
• Volume – Number of shares traded that day

The data is structured in CSV format and is clean, easy to use, and ready for immediate analysis.

Why Use This Dataset?

Whether you're learning data science, building a financial model, or exploring machine learning in the real world, this dataset is a goldmine of insights. Netflix's market history includes:

• Periods of explosive growth during digital transformation
• Volatility during market crashes and global events (e.g., 2008, COVID-19)
• Strategic pivots such as the shift to original content
• Market reactions to earnings, acquisitions, and subscriber milestones

This makes the dataset ideal for:

• Time-series forecasting (ARIMA, Prophet, LSTM)
• Technical and trend analysis (moving averages, RSI, Bollinger Bands)
• Predictive modeling with machine learning
• Investment simulation projects
• Stock market visualization and storytelling
• Financial dashboards (Tableau, Power BI, Streamlit, etc.)

Who Can Use It?

This dataset is designed for:

• Aspiring data scientists practicing EDA and modeling
• Financial analysts and traders exploring trends
• AI researchers working on time-series models
• Students building ML projects
• Developers creating stock visualization tools
• Kaggle competitors seeking real-world datasets

Data Source & Credits

The dataset is derived from publicly available historical stock price data, such as Yahoo Finance, and has been cleaned and organized for educational and research purposes. It is continuously maintained to ensure accuracy.

Start Exploring

Netflix’s rise is more than just a business story — it’s a data-driven journey. With this dataset, you can analyze the company’s stock behavior, train models to predict future trends, or simply visualize how tech reshapes the market.

BackgroundMental health conditions and psychiatric disorders are among the leading causes of illness, disability, and death among young people around the globe. In the United States, teen suicide has increased by about 30% in the last decade. Raising awareness of warning signs and promoting access to mental health resources can help reduce suicide rates for at-risk youth. However, death by suicide remains a taboo topic for public discourse and societal intervention. An unconventional approach to address taboo topics in society is the use of popular media.MethodWe conducted a quantitative content analysis of mainstream news reporting on the controversial Netflix series 13 Reasons Why Season 1. Using a combination of top-down and bottom-up search strategies, our final sample consisted of 97 articles published between March 31 and May 31, 2017, from 16 media outlets in 3,150 sentences. We systematically examined the news framing in these articles in terms of content and valence, the salience of health/social issue related frames, and their compliance with the WHO guidelines.ResultsNearly a third of the content directly addressed issues of our interest: 61.6% was about suicide and 38.4% was about depression, bullying, sexual assault, and other related health/social issues; it was more negative (42.8%) than positive (17.4%). The criticism focused on the risk of suicide contagion, glamorizing teen suicide, and the portrayal of parents and educators as indifferent and incompetent. The praise was about the show raising awareness of real and difficult issues young people struggle with in their everyday life and serving as a conversation starter to spur meaningful discussions. Our evaluation of WHO guideline compliance for reporting on suicide yielded mixed results. Although we found recommended practices across all major categories, they were minimal and could be improved.ConclusionDespite their well intentions and best efforts, the 13 Reasons Why production team missed several critical opportunities to be better prepared and more effective in creating social impact entertainment and fostering difficult dialogs. There is an urgent need to train news reporters about established health communication guidelines and promote best practices in media reporting on sensitive topics such as suicide.

Content

This is a huge dataset that contains every web series around the globe streaming right now at the date of the creation of the dataset.

Inspiration

This dataset can be used to answer the following questions: - Which streaming platform(s) can I find this web series on? - Average IMDb rating and other ratings - What is the genre of the title? - What is the synopsis? - How many seasons are there right now? - Which year this was produced?

Context

Netfilx prize data is one of the popular datasets available today for OTT Recommandation. Netflix Prize Dataset contains title, userid, rating,date of rating as the only attributes for recommandation . we extend the Netflix prize dataset by scraping IMDB data about the titles in Netflix prize dataset. Any copyyright to the scraped data belongs to its respective owners.

Content

The Dataset contains information of approximately 9000 movies and tv shows available in Netflix prize datasets. Information like duration of movie, cast and crew,genre,languages,etc are present. For Columns which hold multiple values in a row arrays have been used to store those values. Please use the .json file to access the dataset to avoid string related errors.

Inspiration

Could you build a Hybrid recommandation system by combining our dataset along with Netflix Prize Dataset.

Update 1

Some movies present in imdb.csv and imdb.json have information of movies with titles same as in Netflix Prize Dataset but were made after 2005 (release of Netflix Prize Dataset) this has been corrected in imdb_processed.csv and imdb_processed.json . Please use this processed data while using the dataset for tasks specific to Netfilx Prize Dataset.

Link to Netflix Prize Dataset

https://www.kaggle.com/netflix-inc/netflix-prize-data

In this project, I perform an Exploratory Data Analysis (EDA) on the Netflix dataset to identify trends in content types, genres, release years, and countries. The goal is to visualize how Netflix's catalog has evolved over time and uncover patterns in the types of shows and movies being produced.